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    Mixture of mlp-experts for trend forecasting of time series: A case study of the tehran stock exchange

    , Article International Journal of Forecasting ; Volume 27, Issue 3 , 2011 , Pages 804-816 ; 01692070 (ISSN) Ebrahimpour, R ; Nikoo, H ; Masoudnia, S ; Yousefi, M. R ; Ghaemi, M. S ; Sharif University of Technology
    A new method for forecasting the trend of time series, based on mixture of MLP experts, is presented. In this paper, three neural network combining methods and an Adaptive Network-Based Fuzzy Inference System (ANFIS) are applied to trend forecasting in the Tehran stock exchange. There are two experiments in this study. In experiment I, the time series data are the Kharg petrochemical company's daily closing prices on the Tehran stock exchange. In this case study, which considers different schemes for forecasting the trend of the time series, the recognition rates are 75.97%, 77.13% and 81.64% for stacked generalization, modified stacked generalization and ANFIS, respectively. Using the... 

    A trainable neural network ensemble for ECG beat classification

    , Article World Academy of Science, Engineering and Technology ; Volume 70 , 2010 , Pages 788-794 ; 2010376X (ISSN) Sajedin, A ; Zakernejad, S ; Faridi, S ; Javadi, M ; Ebrahimpour, R ; Sharif University of Technology
    This paper illustrates the use of a combined neural network model for classification of electrocardiogram (ECG) beats. We present a trainable neural network ensemble approach to develop customized electrocardiogram beat classifier in an effort to further improve the performance of ECG processing and to offer individualized health care. We process a three stage technique for detection of premature ventricular contraction (PVC) from normal beats and other heart diseases. This method includes a denoising, a feature extraction and a classification. At first we investigate the application of stationary wavelet transform (SWT) for noise reduction of the electrocardiogram (ECG) signals. Then...